Overview

Dataset statistics

Number of variables22
Number of observations14731
Missing cells2923
Missing cells (%)0.9%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory2.4 MiB
Average record size in memory169.0 B

Variable types

Numeric14
Categorical7
Boolean1

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
HomePage is highly overall correlated with ProductDescriptionPageHigh correlation
HomePage_Duration is highly overall correlated with LandingPage_Duration and 2 other fieldsHigh correlation
LandingPage is highly overall correlated with LandingPage_Duration and 1 other fieldsHigh correlation
LandingPage_Duration is highly overall correlated with HomePage_Duration and 3 other fieldsHigh correlation
ProductDescriptionPage is highly overall correlated with HomePage and 3 other fieldsHigh correlation
ProductDescriptionPage_Duration is highly overall correlated with HomePage_Duration and 3 other fieldsHigh correlation
GoogleMetric:Bounce Rates is highly overall correlated with GoogleMetric:Exit RatesHigh correlation
GoogleMetric:Exit Rates is highly overall correlated with GoogleMetric:Bounce RatesHigh correlation
OS is highly overall correlated with SearchEngine and 1 other fieldsHigh correlation
SearchEngine is highly overall correlated with OS and 2 other fieldsHigh correlation
Type of Traffic is highly overall correlated with SearchEngineHigh correlation
CustomerType is highly overall correlated with OS and 1 other fieldsHigh correlation
HomePage has 153 (1.0%) missing valuesMissing
HomePage_Duration has 150 (1.0%) missing valuesMissing
LandingPage has 153 (1.0%) missing valuesMissing
ProductDescriptionPage_Duration has 167 (1.1%) missing valuesMissing
GoogleMetric:Bounce Rates has 151 (1.0%) missing valuesMissing
SeasonalPurchase has 150 (1.0%) missing valuesMissing
HomePage has 6977 (47.4%) zerosZeros
HomePage_Duration has 7130 (48.4%) zerosZeros
LandingPage has 11525 (78.2%) zerosZeros
LandingPage_Duration has 11804 (80.1%) zerosZeros
ProductDescriptionPage_Duration has 957 (6.5%) zerosZeros
GoogleMetric:Bounce Rates has 6409 (43.5%) zerosZeros
GoogleMetric:Page Values has 11787 (80.0%) zerosZeros
SeasonalPurchase has 13034 (88.5%) zerosZeros

Reproduction

Analysis started2023-01-21 18:11:36.863561
Analysis finished2023-01-21 18:12:11.032479
Duration34.17 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

HomePage
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct27
Distinct (%)0.2%
Missing153
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean2.2502401
Minimum0
Maximum27
Zeros6977
Zeros (%)47.4%
Negative0
Negative (%)0.0%
Memory size115.2 KiB
2023-01-21T23:42:11.110489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum27
Range27
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.2880417
Coefficient of variation (CV)1.4611959
Kurtosis5.1596807
Mean2.2502401
Median Absolute Deviation (MAD)1
Skewness2.0335591
Sum32804
Variance10.811218
MonotonicityNot monotonic
2023-01-21T23:42:11.226711image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 6977
47.4%
1 1580
 
10.7%
2 1264
 
8.6%
3 1114
 
7.6%
4 882
 
6.0%
5 700
 
4.8%
6 485
 
3.3%
7 385
 
2.6%
8 303
 
2.1%
9 252
 
1.7%
Other values (17) 636
 
4.3%
ValueCountFrequency (%)
0 6977
47.4%
1 1580
 
10.7%
2 1264
 
8.6%
3 1114
 
7.6%
4 882
 
6.0%
5 700
 
4.8%
6 485
 
3.3%
7 385
 
2.6%
8 303
 
2.1%
9 252
 
1.7%
ValueCountFrequency (%)
27 1
 
< 0.1%
26 1
 
< 0.1%
24 3
 
< 0.1%
23 6
 
< 0.1%
22 7
 
< 0.1%
21 3
 
< 0.1%
20 3
 
< 0.1%
19 7
 
< 0.1%
18 14
0.1%
17 20
0.1%

HomePage_Duration
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct2844
Distinct (%)19.5%
Missing150
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean79.300762
Minimum0
Maximum3398.75
Zeros7130
Zeros (%)48.4%
Negative0
Negative (%)0.0%
Memory size115.2 KiB
2023-01-21T23:42:11.351462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q391
95-th percentile341.42857
Maximum3398.75
Range3398.75
Interquartile range (IQR)91

Descriptive statistics

Standard deviation179.3747
Coefficient of variation (CV)2.2619543
Kurtosis55.120503
Mean79.300762
Median Absolute Deviation (MAD)5
Skewness5.9224868
Sum1156284.4
Variance32175.283
MonotonicityNot monotonic
2023-01-21T23:42:11.484637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7130
48.4%
4 76
 
0.5%
5 62
 
0.4%
11 55
 
0.4%
6 49
 
0.3%
14 44
 
0.3%
10 42
 
0.3%
7 41
 
0.3%
15 40
 
0.3%
9 39
 
0.3%
Other values (2834) 7003
47.5%
(Missing) 150
 
1.0%
ValueCountFrequency (%)
0 7130
48.4%
1.333333333 3
 
< 0.1%
2 17
 
0.1%
3 33
 
0.2%
3.5 5
 
< 0.1%
4 76
 
0.5%
4.5 4
 
< 0.1%
4.75 1
 
< 0.1%
5 62
 
0.4%
5.066666667 1
 
< 0.1%
ValueCountFrequency (%)
3398.75 1
 
< 0.1%
2720.5 1
 
< 0.1%
2657.318056 4
< 0.1%
2629.253968 1
 
< 0.1%
2407.42381 2
< 0.1%
2156.166667 1
 
< 0.1%
2137.112745 1
 
< 0.1%
2086.75 1
 
< 0.1%
2047.234848 1
 
< 0.1%
1951.279141 2
< 0.1%

LandingPage
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct17
Distinct (%)0.1%
Missing153
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.49073947
Minimum0
Maximum24
Zeros11525
Zeros (%)78.2%
Negative0
Negative (%)0.0%
Memory size115.2 KiB
2023-01-21T23:42:11.598062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2523765
Coefficient of variation (CV)2.5520191
Kurtosis28.409076
Mean0.49073947
Median Absolute Deviation (MAD)0
Skewness4.1454294
Sum7154
Variance1.5684469
MonotonicityNot monotonic
2023-01-21T23:42:11.700400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 11525
78.2%
1 1206
 
8.2%
2 859
 
5.8%
3 452
 
3.1%
4 239
 
1.6%
5 119
 
0.8%
6 89
 
0.6%
7 40
 
0.3%
8 13
 
0.1%
9 12
 
0.1%
Other values (7) 24
 
0.2%
(Missing) 153
 
1.0%
ValueCountFrequency (%)
0 11525
78.2%
1 1206
 
8.2%
2 859
 
5.8%
3 452
 
3.1%
4 239
 
1.6%
5 119
 
0.8%
6 89
 
0.6%
7 40
 
0.3%
8 13
 
0.1%
9 12
 
0.1%
ValueCountFrequency (%)
24 1
 
< 0.1%
16 2
 
< 0.1%
14 2
 
< 0.1%
13 4
 
< 0.1%
12 5
 
< 0.1%
11 1
 
< 0.1%
10 9
 
0.1%
9 12
 
0.1%
8 13
 
0.1%
7 40
0.3%

LandingPage_Duration
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct1084
Distinct (%)7.4%
Missing135
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean33.455943
Minimum0
Maximum2549.375
Zeros11804
Zeros (%)80.1%
Negative0
Negative (%)0.0%
Memory size115.2 KiB
2023-01-21T23:42:11.817485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile187
Maximum2549.375
Range2549.375
Interquartile range (IQR)0

Descriptive statistics

Standard deviation140.14626
Coefficient of variation (CV)4.1889794
Kurtosis85.65435
Mean33.455943
Median Absolute Deviation (MAD)0
Skewness7.9979972
Sum488322.94
Variance19640.973
MonotonicityNot monotonic
2023-01-21T23:42:11.954412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11804
80.1%
13 35
 
0.2%
10 34
 
0.2%
9 33
 
0.2%
11 30
 
0.2%
16 28
 
0.2%
12 27
 
0.2%
8 25
 
0.2%
6 25
 
0.2%
7 24
 
0.2%
Other values (1074) 2531
 
17.2%
(Missing) 135
 
0.9%
ValueCountFrequency (%)
0 11804
80.1%
1 2
 
< 0.1%
1.5 1
 
< 0.1%
2 12
 
0.1%
2.5 3
 
< 0.1%
3 16
 
0.1%
4 19
 
0.1%
5 19
 
0.1%
5.5 4
 
< 0.1%
6 25
 
0.2%
ValueCountFrequency (%)
2549.375 2
< 0.1%
2256.916667 1
 
< 0.1%
2252.033333 1
 
< 0.1%
2195.3 1
 
< 0.1%
2166.5 2
< 0.1%
2050.433333 1
 
< 0.1%
1949.166667 4
< 0.1%
1830.5 1
 
< 0.1%
1779.166667 1
 
< 0.1%
1778 1
 
< 0.1%

ProductDescriptionPage
Real number (ℝ)

Distinct294
Distinct (%)2.0%
Missing123
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean31.559488
Minimum0
Maximum705
Zeros46
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size115.2 KiB
2023-01-21T23:42:12.093129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median17.5
Q338
95-th percentile111
Maximum705
Range705
Interquartile range (IQR)31

Descriptive statistics

Standard deviation44.897089
Coefficient of variation (CV)1.4226178
Kurtosis33.034005
Mean31.559488
Median Absolute Deviation (MAD)12.5
Skewness4.4590316
Sum461021
Variance2015.7486
MonotonicityNot monotonic
2023-01-21T23:42:12.225578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 785
 
5.3%
2 564
 
3.8%
3 554
 
3.8%
6 506
 
3.4%
4 501
 
3.4%
7 469
 
3.2%
5 452
 
3.1%
8 416
 
2.8%
10 410
 
2.8%
9 372
 
2.5%
Other values (284) 9579
65.0%
ValueCountFrequency (%)
0 46
 
0.3%
1 785
5.3%
2 564
3.8%
3 554
3.8%
4 501
3.4%
5 452
3.1%
6 506
3.4%
7 469
3.2%
8 416
2.8%
9 372
2.5%
ValueCountFrequency (%)
705 1
< 0.1%
686 2
< 0.1%
584 1
< 0.1%
534 1
< 0.1%
518 1
< 0.1%
517 1
< 0.1%
501 2
< 0.1%
486 2
< 0.1%
470 1
< 0.1%
449 2
< 0.1%

ProductDescriptionPage_Duration
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct7933
Distinct (%)54.5%
Missing167
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean1184.3461
Minimum0
Maximum63973.522
Zeros957
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size115.2 KiB
2023-01-21T23:42:12.366841image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1173.1875
median584.33333
Q31434.2551
95-th percentile4232.5228
Maximum63973.522
Range63973.522
Interquartile range (IQR)1261.0676

Descriptive statistics

Standard deviation2009.4963
Coefficient of variation (CV)1.6967138
Kurtosis173.541
Mean1184.3461
Median Absolute Deviation (MAD)493.25583
Skewness8.5807537
Sum17248816
Variance4038075.4
MonotonicityNot monotonic
2023-01-21T23:42:12.497420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 957
 
6.5%
17 27
 
0.2%
11 21
 
0.1%
8 21
 
0.1%
7 20
 
0.1%
14 20
 
0.1%
15 19
 
0.1%
60 18
 
0.1%
63 17
 
0.1%
50 17
 
0.1%
Other values (7923) 13427
91.1%
(Missing) 167
 
1.1%
ValueCountFrequency (%)
0 957
6.5%
0.5 2
 
< 0.1%
1 2
 
< 0.1%
2.333333333 2
 
< 0.1%
2.666666667 1
 
< 0.1%
3 3
 
< 0.1%
4 11
 
0.1%
5 10
 
0.1%
5.333333333 1
 
< 0.1%
6 8
 
0.1%
ValueCountFrequency (%)
63973.52223 2
< 0.1%
43171.23338 1
 
< 0.1%
29970.46597 4
< 0.1%
27009.85943 1
 
< 0.1%
24844.1562 1
 
< 0.1%
23888.81 1
 
< 0.1%
23342.08205 2
< 0.1%
23050.10414 2
< 0.1%
21857.04648 2
< 0.1%
21672.24425 2
< 0.1%

GoogleMetric:Bounce Rates
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct1628
Distinct (%)11.2%
Missing151
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.023365957
Minimum0
Maximum0.2
Zeros6409
Zeros (%)43.5%
Negative0
Negative (%)0.0%
Memory size115.2 KiB
2023-01-21T23:42:12.637719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.003478261
Q30.018181818
95-th percentile0.2
Maximum0.2
Range0.2
Interquartile range (IQR)0.018181818

Descriptive statistics

Standard deviation0.050010552
Coefficient of variation (CV)2.1403169
Kurtosis6.952879
Mean0.023365957
Median Absolute Deviation (MAD)0.003478261
Skewness2.8306078
Sum340.67565
Variance0.0025010554
MonotonicityNot monotonic
2023-01-21T23:42:12.780676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6409
43.5%
0.2 895
 
6.1%
0.066666667 160
 
1.1%
0.05 135
 
0.9%
0.028571429 132
 
0.9%
0.033333333 124
 
0.8%
0.1 121
 
0.8%
0.016666667 120
 
0.8%
0.025 115
 
0.8%
0.022222222 111
 
0.8%
Other values (1618) 6258
42.5%
(Missing) 151
 
1.0%
ValueCountFrequency (%)
0 6409
43.5%
3.35 × 10-52
 
< 0.1%
3.83 × 10-51
 
< 0.1%
3.94 × 10-51
 
< 0.1%
7.09 × 10-51
 
< 0.1%
7.27 × 10-51
 
< 0.1%
7.5 × 10-51
 
< 0.1%
8.01 × 10-51
 
< 0.1%
8.08 × 10-51
 
< 0.1%
0.000123762 1
 
< 0.1%
ValueCountFrequency (%)
0.2 895
6.1%
0.183333333 1
 
< 0.1%
0.18 6
 
< 0.1%
0.175 2
 
< 0.1%
0.166666667 5
 
< 0.1%
0.164285714 1
 
< 0.1%
0.164230769 2
 
< 0.1%
0.161904762 1
 
< 0.1%
0.16 4
 
< 0.1%
0.155555556 5
 
< 0.1%

GoogleMetric:Exit Rates
Real number (ℝ)

Distinct4051
Distinct (%)27.7%
Missing129
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean0.044664125
Minimum0
Maximum0.2
Zeros77
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size115.2 KiB
2023-01-21T23:42:12.929097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.004761905
Q10.014501543
median0.026406456
Q30.05
95-th percentile0.2
Maximum0.2
Range0.2
Interquartile range (IQR)0.035498457

Descriptive statistics

Standard deviation0.049912317
Coefficient of variation (CV)1.1175035
Kurtosis3.559785
Mean0.044664125
Median Absolute Deviation (MAD)0.01464175
Skewness2.0677222
Sum652.18556
Variance0.0024912394
MonotonicityNot monotonic
2023-01-21T23:42:13.071544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 913
 
6.2%
0.05 410
 
2.8%
0.1 404
 
2.7%
0.033333333 339
 
2.3%
0.066666667 313
 
2.1%
0.04 267
 
1.8%
0.025 266
 
1.8%
0.022222222 207
 
1.4%
0.02 201
 
1.4%
0.016666667 199
 
1.4%
Other values (4041) 11083
75.2%
ValueCountFrequency (%)
0 77
0.5%
0.000175593 1
 
< 0.1%
0.000250438 2
 
< 0.1%
0.000262123 2
 
< 0.1%
0.000263158 1
 
< 0.1%
0.000292398 2
 
< 0.1%
0.000409836 1
 
< 0.1%
0.000446429 2
 
< 0.1%
0.000468384 2
 
< 0.1%
0.000480769 1
 
< 0.1%
ValueCountFrequency (%)
0.2 913
6.2%
0.188888889 4
 
< 0.1%
0.186666667 6
 
< 0.1%
0.183333333 3
 
< 0.1%
0.18034188 1
 
< 0.1%
0.18 2
 
< 0.1%
0.177777778 8
 
0.1%
0.175 5
 
< 0.1%
0.173809524 1
 
< 0.1%
0.173333333 1
 
< 0.1%

GoogleMetric:Page Values
Real number (ℝ)

Distinct2120
Distinct (%)14.5%
Missing132
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean4.8126205
Minimum0
Maximum361.76374
Zeros11787
Zeros (%)80.0%
Negative0
Negative (%)0.0%
Memory size115.2 KiB
2023-01-21T23:42:13.217945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile31.768223
Maximum361.76374
Range361.76374
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16.887366
Coefficient of variation (CV)3.5089753
Kurtosis94.521451
Mean4.8126205
Median Absolute Deviation (MAD)0
Skewness7.5881032
Sum70259.446
Variance285.18315
MonotonicityNot monotonic
2023-01-21T23:42:13.353148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11787
80.0%
42.29306752 5
 
< 0.1%
53.988 5
 
< 0.1%
4.212415755 4
 
< 0.1%
6.733744751 4
 
< 0.1%
6.099899016 4
 
< 0.1%
10.48533333 4
 
< 0.1%
42.8662809 4
 
< 0.1%
10.80188719 4
 
< 0.1%
32.4141704 4
 
< 0.1%
Other values (2110) 2774
 
18.8%
(Missing) 132
 
0.9%
ValueCountFrequency (%)
0 11787
80.0%
0.067049546 1
 
< 0.1%
0.093546949 1
 
< 0.1%
0.098621403 1
 
< 0.1%
0.131837013 2
 
< 0.1%
0.139200623 1
 
< 0.1%
0.150650498 2
 
< 0.1%
0.154821253 1
 
< 0.1%
0.17982681 1
 
< 0.1%
0.25272174 1
 
< 0.1%
ValueCountFrequency (%)
361.7637419 1
 
< 0.1%
360.9533839 2
< 0.1%
287.9537928 1
 
< 0.1%
270.7846931 1
 
< 0.1%
255.5691579 2
< 0.1%
254.6071579 1
 
< 0.1%
246.7585902 3
< 0.1%
239.98 1
 
< 0.1%
218.8649096 2
< 0.1%
218.3951915 2
< 0.1%

SeasonalPurchase
Real number (ℝ)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing150
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.064083396
Minimum0
Maximum1
Zeros13034
Zeros (%)88.5%
Negative0
Negative (%)0.0%
Memory size115.2 KiB
2023-01-21T23:42:13.474290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.6
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.20258285
Coefficient of variation (CV)3.1612378
Kurtosis9.3345247
Mean0.064083396
Median Absolute Deviation (MAD)0
Skewness3.217069
Sum934.4
Variance0.041039813
MonotonicityNot monotonic
2023-01-21T23:42:13.875580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 13034
88.5%
0.6 427
 
2.9%
0.8 398
 
2.7%
0.4 313
 
2.1%
0.2 218
 
1.5%
1 191
 
1.3%
(Missing) 150
 
1.0%
ValueCountFrequency (%)
0 13034
88.5%
0.2 218
 
1.5%
0.4 313
 
2.1%
0.6 427
 
2.9%
0.8 398
 
2.7%
1 191
 
1.3%
ValueCountFrequency (%)
1 191
 
1.3%
0.8 398
 
2.7%
0.6 427
 
2.9%
0.4 313
 
2.1%
0.2 218
 
1.5%
0 13034
88.5%
Distinct10
Distinct (%)0.1%
Missing144
Missing (%)1.0%
Memory size115.2 KiB
May
4121 
Nov
3439 
Mar
2300 
Dec
2013 
Oct
628 
Other values (5)
2086 

Length

Max length4
Median length3
Mean length3.0239254
Min length3

Characters and Unicode

Total characters44110
Distinct characters22
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFeb
2nd rowFeb
3rd rowFeb
4th rowFeb
5th rowFeb

Common Values

ValueCountFrequency (%)
May 4121
28.0%
Nov 3439
23.3%
Mar 2300
15.6%
Dec 2013
13.7%
Oct 628
 
4.3%
Sep 519
 
3.5%
Aug 510
 
3.5%
Jul 487
 
3.3%
June 349
 
2.4%
Feb 221
 
1.5%
(Missing) 144
 
1.0%

Length

2023-01-21T23:42:13.976324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-21T23:42:14.182582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
may 4121
28.3%
nov 3439
23.6%
mar 2300
15.8%
dec 2013
13.8%
oct 628
 
4.3%
sep 519
 
3.6%
aug 510
 
3.5%
jul 487
 
3.3%
june 349
 
2.4%
feb 221
 
1.5%

Most occurring characters

ValueCountFrequency (%)
M 6421
14.6%
a 6421
14.6%
y 4121
9.3%
N 3439
7.8%
o 3439
7.8%
v 3439
7.8%
e 3102
7.0%
c 2641
 
6.0%
r 2300
 
5.2%
D 2013
 
4.6%
Other values (12) 6774
15.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 29523
66.9%
Uppercase Letter 14587
33.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6421
21.7%
y 4121
14.0%
o 3439
11.6%
v 3439
11.6%
e 3102
10.5%
c 2641
8.9%
r 2300
 
7.8%
u 1346
 
4.6%
t 628
 
2.1%
p 519
 
1.8%
Other values (4) 1567
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
M 6421
44.0%
N 3439
23.6%
D 2013
 
13.8%
J 836
 
5.7%
O 628
 
4.3%
S 519
 
3.6%
A 510
 
3.5%
F 221
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 44110
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 6421
14.6%
a 6421
14.6%
y 4121
9.3%
N 3439
7.8%
o 3439
7.8%
v 3439
7.8%
e 3102
7.0%
c 2641
 
6.0%
r 2300
 
5.2%
D 2013
 
4.6%
Other values (12) 6774
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 6421
14.6%
a 6421
14.6%
y 4121
9.3%
N 3439
7.8%
o 3439
7.8%
v 3439
7.8%
e 3102
7.0%
c 2641
 
6.0%
r 2300
 
5.2%
D 2013
 
4.6%
Other values (12) 6774
15.4%

OS
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing134
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean2.1224224
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size115.2 KiB
2023-01-21T23:42:14.322688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile3
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.91440435
Coefficient of variation (CV)0.43083052
Kurtosis10.529658
Mean2.1224224
Median Absolute Deviation (MAD)0
Skewness2.0876368
Sum30981
Variance0.83613532
MonotonicityNot monotonic
2023-01-21T23:42:14.461282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 7832
53.2%
1 3072
 
20.9%
3 2987
 
20.3%
4 572
 
3.9%
8 96
 
0.7%
6 26
 
0.2%
7 6
 
< 0.1%
5 6
 
< 0.1%
(Missing) 134
 
0.9%
ValueCountFrequency (%)
1 3072
 
20.9%
2 7832
53.2%
3 2987
 
20.3%
4 572
 
3.9%
5 6
 
< 0.1%
6 26
 
0.2%
7 6
 
< 0.1%
8 96
 
0.7%
ValueCountFrequency (%)
8 96
 
0.7%
7 6
 
< 0.1%
6 26
 
0.2%
5 6
 
< 0.1%
4 572
 
3.9%
3 2987
 
20.3%
2 7832
53.2%
1 3072
 
20.9%

SearchEngine
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing122
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean2.3566295
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size115.2 KiB
2023-01-21T23:42:14.571708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile5
Maximum13
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7218229
Coefficient of variation (CV)0.73062946
Kurtosis12.590887
Mean2.3566295
Median Absolute Deviation (MAD)0
Skewness3.2247015
Sum34428
Variance2.9646742
MonotonicityNot monotonic
2023-01-21T23:42:14.681947image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 9382
63.7%
1 2959
 
20.1%
4 879
 
6.0%
5 539
 
3.7%
6 211
 
1.4%
10 200
 
1.4%
8 154
 
1.0%
3 127
 
0.9%
13 73
 
0.5%
7 69
 
0.5%
Other values (3) 16
 
0.1%
(Missing) 122
 
0.8%
ValueCountFrequency (%)
1 2959
 
20.1%
2 9382
63.7%
3 127
 
0.9%
4 879
 
6.0%
5 539
 
3.7%
6 211
 
1.4%
7 69
 
0.5%
8 154
 
1.0%
9 1
 
< 0.1%
10 200
 
1.4%
ValueCountFrequency (%)
13 73
 
0.5%
12 9
 
0.1%
11 6
 
< 0.1%
10 200
 
1.4%
9 1
 
< 0.1%
8 154
 
1.0%
7 69
 
0.5%
6 211
 
1.4%
5 539
3.7%
4 879
6.0%

Zone
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing117
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean3.1556726
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size115.2 KiB
2023-01-21T23:42:14.787687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q34
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4051549
Coefficient of variation (CV)0.76216869
Kurtosis-0.18028937
Mean3.1556726
Median Absolute Deviation (MAD)2
Skewness0.97320412
Sum46117
Variance5.78477
MonotonicityNot monotonic
2023-01-21T23:42:14.927296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 5648
38.3%
3 2853
19.4%
4 1374
 
9.3%
2 1353
 
9.2%
6 970
 
6.6%
7 943
 
6.4%
9 601
 
4.1%
8 506
 
3.4%
5 366
 
2.5%
(Missing) 117
 
0.8%
ValueCountFrequency (%)
1 5648
38.3%
2 1353
 
9.2%
3 2853
19.4%
4 1374
 
9.3%
5 366
 
2.5%
6 970
 
6.6%
7 943
 
6.4%
8 506
 
3.4%
9 601
 
4.1%
ValueCountFrequency (%)
9 601
 
4.1%
8 506
 
3.4%
7 943
 
6.4%
6 970
 
6.6%
5 366
 
2.5%
4 1374
 
9.3%
3 2853
19.4%
2 1353
 
9.2%
1 5648
38.3%

Type of Traffic
Real number (ℝ)

Distinct20
Distinct (%)0.1%
Missing143
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean4.0901426
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size115.2 KiB
2023-01-21T23:42:15.044282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile13
Maximum20
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.0401466
Coefficient of variation (CV)0.98777648
Kurtosis3.2862002
Mean4.0901426
Median Absolute Deviation (MAD)1
Skewness1.9285814
Sum59667
Variance16.322785
MonotonicityNot monotonic
2023-01-21T23:42:15.193933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 4553
30.9%
1 2919
19.8%
3 2463
16.7%
4 1300
 
8.8%
13 930
 
6.3%
10 546
 
3.7%
6 505
 
3.4%
8 388
 
2.6%
11 284
 
1.9%
5 284
 
1.9%
Other values (10) 416
 
2.8%
ValueCountFrequency (%)
1 2919
19.8%
2 4553
30.9%
3 2463
16.7%
4 1300
 
8.8%
5 284
 
1.9%
6 505
 
3.4%
7 42
 
0.3%
8 388
 
2.6%
9 52
 
0.4%
10 546
 
3.7%
ValueCountFrequency (%)
20 225
 
1.5%
19 20
 
0.1%
18 13
 
0.1%
17 1
 
< 0.1%
16 4
 
< 0.1%
15 44
 
0.3%
14 14
 
0.1%
13 930
6.3%
12 1
 
< 0.1%
11 284
 
1.9%

CustomerType
Categorical

Distinct3
Distinct (%)< 0.1%
Missing144
Missing (%)1.0%
Memory size115.2 KiB
Returning_Visitor
12550 
New_Visitor
1925 
Other
 
112

Length

Max length17
Median length17
Mean length16.116062
Min length5

Characters and Unicode

Total characters235085
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReturning_Visitor
2nd rowReturning_Visitor
3rd rowReturning_Visitor
4th rowReturning_Visitor
5th rowReturning_Visitor

Common Values

ValueCountFrequency (%)
Returning_Visitor 12550
85.2%
New_Visitor 1925
 
13.1%
Other 112
 
0.8%
(Missing) 144
 
1.0%

Length

2023-01-21T23:42:15.399949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-21T23:42:15.601728image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
returning_visitor 12550
86.0%
new_visitor 1925
 
13.2%
other 112
 
0.8%

Most occurring characters

ValueCountFrequency (%)
i 41500
17.7%
t 27137
11.5%
r 27137
11.5%
n 25100
10.7%
e 14587
 
6.2%
_ 14475
 
6.2%
V 14475
 
6.2%
s 14475
 
6.2%
o 14475
 
6.2%
R 12550
 
5.3%
Other values (6) 29174
12.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 191548
81.5%
Uppercase Letter 29062
 
12.4%
Connector Punctuation 14475
 
6.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 41500
21.7%
t 27137
14.2%
r 27137
14.2%
n 25100
13.1%
e 14587
 
7.6%
s 14475
 
7.6%
o 14475
 
7.6%
u 12550
 
6.6%
g 12550
 
6.6%
w 1925
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
V 14475
49.8%
R 12550
43.2%
N 1925
 
6.6%
O 112
 
0.4%
Connector Punctuation
ValueCountFrequency (%)
_ 14475
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 220610
93.8%
Common 14475
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 41500
18.8%
t 27137
12.3%
r 27137
12.3%
n 25100
11.4%
e 14587
 
6.6%
V 14475
 
6.6%
s 14475
 
6.6%
o 14475
 
6.6%
R 12550
 
5.7%
u 12550
 
5.7%
Other values (5) 16624
7.5%
Common
ValueCountFrequency (%)
_ 14475
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 235085
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 41500
17.7%
t 27137
11.5%
r 27137
11.5%
n 25100
10.7%
e 14587
 
6.2%
_ 14475
 
6.2%
V 14475
 
6.2%
s 14475
 
6.2%
o 14475
 
6.2%
R 12550
 
5.3%
Other values (6) 29174
12.4%

Gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing145
Missing (%)1.0%
Memory size115.2 KiB
Not Specified
4971 
Female
4829 
Male
4786 

Length

Max length13
Median length6
Mean length7.7293981
Min length4

Characters and Unicode

Total characters112741
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Specified
2nd rowFemale
3rd rowFemale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Not Specified 4971
33.7%
Female 4829
32.8%
Male 4786
32.5%
(Missing) 145
 
1.0%

Length

2023-01-21T23:42:15.741968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-21T23:42:15.874916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
not 4971
25.4%
specified 4971
25.4%
female 4829
24.7%
male 4786
24.5%

Most occurring characters

ValueCountFrequency (%)
e 24386
21.6%
i 9942
 
8.8%
a 9615
 
8.5%
l 9615
 
8.5%
N 4971
 
4.4%
o 4971
 
4.4%
t 4971
 
4.4%
4971
 
4.4%
S 4971
 
4.4%
p 4971
 
4.4%
Other values (6) 29357
26.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 88213
78.2%
Uppercase Letter 19557
 
17.3%
Space Separator 4971
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 24386
27.6%
i 9942
11.3%
a 9615
 
10.9%
l 9615
 
10.9%
o 4971
 
5.6%
t 4971
 
5.6%
p 4971
 
5.6%
c 4971
 
5.6%
f 4971
 
5.6%
d 4971
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
N 4971
25.4%
S 4971
25.4%
F 4829
24.7%
M 4786
24.5%
Space Separator
ValueCountFrequency (%)
4971
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 107770
95.6%
Common 4971
 
4.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 24386
22.6%
i 9942
9.2%
a 9615
 
8.9%
l 9615
 
8.9%
N 4971
 
4.6%
o 4971
 
4.6%
t 4971
 
4.6%
S 4971
 
4.6%
p 4971
 
4.6%
c 4971
 
4.6%
Other values (5) 24386
22.6%
Common
ValueCountFrequency (%)
4971
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 112741
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 24386
21.6%
i 9942
 
8.8%
a 9615
 
8.5%
l 9615
 
8.5%
N 4971
 
4.4%
o 4971
 
4.4%
t 4971
 
4.4%
4971
 
4.4%
S 4971
 
4.4%
p 4971
 
4.4%
Other values (6) 29357
26.0%

Cookies Setting
Categorical

Distinct3
Distinct (%)< 0.1%
Missing144
Missing (%)1.0%
Memory size115.2 KiB
Deny
4964 
Required
4867 
ALL
4756 

Length

Max length8
Median length4
Mean length5.0085693
Min length3

Characters and Unicode

Total characters73060
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDeny
2nd rowDeny
3rd rowALL
4th rowALL
5th rowDeny

Common Values

ValueCountFrequency (%)
Deny 4964
33.7%
Required 4867
33.0%
ALL 4756
32.3%
(Missing) 144
 
1.0%

Length

2023-01-21T23:42:16.032491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-21T23:42:16.207023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
deny 4964
34.0%
required 4867
33.4%
all 4756
32.6%

Most occurring characters

ValueCountFrequency (%)
e 14698
20.1%
L 9512
13.0%
D 4964
 
6.8%
n 4964
 
6.8%
y 4964
 
6.8%
R 4867
 
6.7%
q 4867
 
6.7%
u 4867
 
6.7%
i 4867
 
6.7%
r 4867
 
6.7%
Other values (2) 9623
13.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 48961
67.0%
Uppercase Letter 24099
33.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 14698
30.0%
n 4964
 
10.1%
y 4964
 
10.1%
q 4867
 
9.9%
u 4867
 
9.9%
i 4867
 
9.9%
r 4867
 
9.9%
d 4867
 
9.9%
Uppercase Letter
ValueCountFrequency (%)
L 9512
39.5%
D 4964
20.6%
R 4867
20.2%
A 4756
19.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 73060
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 14698
20.1%
L 9512
13.0%
D 4964
 
6.8%
n 4964
 
6.8%
y 4964
 
6.8%
R 4867
 
6.7%
q 4867
 
6.7%
u 4867
 
6.7%
i 4867
 
6.7%
r 4867
 
6.7%
Other values (2) 9623
13.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73060
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 14698
20.1%
L 9512
13.0%
D 4964
 
6.8%
n 4964
 
6.8%
y 4964
 
6.8%
R 4867
 
6.7%
q 4867
 
6.7%
u 4867
 
6.7%
i 4867
 
6.7%
r 4867
 
6.7%
Other values (2) 9623
13.2%

Education
Categorical

Distinct4
Distinct (%)< 0.1%
Missing136
Missing (%)0.9%
Memory size115.2 KiB
Others
3726 
Graduate
3691 
Diploma
3653 
Not Specified
3525 

Length

Max length13
Median length8
Mean length8.4467283
Min length6

Characters and Unicode

Total characters123280
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Specified
2nd rowOthers
3rd rowOthers
4th rowDiploma
5th rowDiploma

Common Values

ValueCountFrequency (%)
Others 3726
25.3%
Graduate 3691
25.1%
Diploma 3653
24.8%
Not Specified 3525
23.9%
(Missing) 136
 
0.9%

Length

2023-01-21T23:42:16.420014image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-21T23:42:16.661429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
others 3726
20.6%
graduate 3691
20.4%
diploma 3653
20.2%
not 3525
19.5%
specified 3525
19.5%

Most occurring characters

ValueCountFrequency (%)
e 14467
 
11.7%
a 11035
 
9.0%
t 10942
 
8.9%
i 10703
 
8.7%
r 7417
 
6.0%
d 7216
 
5.9%
o 7178
 
5.8%
p 7178
 
5.8%
O 3726
 
3.0%
h 3726
 
3.0%
Other values (11) 39692
32.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 101635
82.4%
Uppercase Letter 18120
 
14.7%
Space Separator 3525
 
2.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 14467
14.2%
a 11035
10.9%
t 10942
10.8%
i 10703
10.5%
r 7417
7.3%
d 7216
 
7.1%
o 7178
 
7.1%
p 7178
 
7.1%
h 3726
 
3.7%
s 3726
 
3.7%
Other values (5) 18047
17.8%
Uppercase Letter
ValueCountFrequency (%)
O 3726
20.6%
G 3691
20.4%
D 3653
20.2%
N 3525
19.5%
S 3525
19.5%
Space Separator
ValueCountFrequency (%)
3525
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 119755
97.1%
Common 3525
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 14467
12.1%
a 11035
 
9.2%
t 10942
 
9.1%
i 10703
 
8.9%
r 7417
 
6.2%
d 7216
 
6.0%
o 7178
 
6.0%
p 7178
 
6.0%
O 3726
 
3.1%
h 3726
 
3.1%
Other values (10) 36167
30.2%
Common
ValueCountFrequency (%)
3525
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123280
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 14467
 
11.7%
a 11035
 
9.0%
t 10942
 
8.9%
i 10703
 
8.7%
r 7417
 
6.0%
d 7216
 
5.9%
o 7178
 
5.8%
p 7178
 
5.8%
O 3726
 
3.0%
h 3726
 
3.0%
Other values (11) 39692
32.2%

Marital Status
Categorical

Distinct3
Distinct (%)< 0.1%
Missing130
Missing (%)0.9%
Memory size115.2 KiB
Other
4952 
Single
4919 
Married
4730 

Length

Max length7
Median length6
Mean length5.9847956
Min length5

Characters and Unicode

Total characters87384
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther
2nd rowMarried
3rd rowMarried
4th rowSingle
5th rowOther

Common Values

ValueCountFrequency (%)
Other 4952
33.6%
Single 4919
33.4%
Married 4730
32.1%
(Missing) 130
 
0.9%

Length

2023-01-21T23:42:16.836899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-21T23:42:17.004840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
other 4952
33.9%
single 4919
33.7%
married 4730
32.4%

Most occurring characters

ValueCountFrequency (%)
e 14601
16.7%
r 14412
16.5%
i 9649
11.0%
O 4952
 
5.7%
t 4952
 
5.7%
h 4952
 
5.7%
S 4919
 
5.6%
n 4919
 
5.6%
g 4919
 
5.6%
l 4919
 
5.6%
Other values (3) 14190
16.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 72783
83.3%
Uppercase Letter 14601
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 14601
20.1%
r 14412
19.8%
i 9649
13.3%
t 4952
 
6.8%
h 4952
 
6.8%
n 4919
 
6.8%
g 4919
 
6.8%
l 4919
 
6.8%
a 4730
 
6.5%
d 4730
 
6.5%
Uppercase Letter
ValueCountFrequency (%)
O 4952
33.9%
S 4919
33.7%
M 4730
32.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 87384
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 14601
16.7%
r 14412
16.5%
i 9649
11.0%
O 4952
 
5.7%
t 4952
 
5.7%
h 4952
 
5.7%
S 4919
 
5.6%
n 4919
 
5.6%
g 4919
 
5.6%
l 4919
 
5.6%
Other values (3) 14190
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 87384
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 14601
16.7%
r 14412
16.5%
i 9649
11.0%
O 4952
 
5.7%
t 4952
 
5.7%
h 4952
 
5.7%
S 4919
 
5.6%
n 4919
 
5.6%
g 4919
 
5.6%
l 4919
 
5.6%
Other values (3) 14190
16.2%

WeekendPurchase
Categorical

Distinct2
Distinct (%)< 0.1%
Missing121
Missing (%)0.8%
Memory size115.2 KiB
0.0
11189 
1.0
3421 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters43830
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 11189
76.0%
1.0 3421
 
23.2%
(Missing) 121
 
0.8%

Length

2023-01-21T23:42:17.136728image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-21T23:42:17.244966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11189
76.6%
1.0 3421
 
23.4%

Most occurring characters

ValueCountFrequency (%)
0 25799
58.9%
. 14610
33.3%
1 3421
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29220
66.7%
Other Punctuation 14610
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25799
88.3%
1 3421
 
11.7%
Other Punctuation
ValueCountFrequency (%)
. 14610
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 43830
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25799
58.9%
. 14610
33.3%
1 3421
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43830
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25799
58.9%
. 14610
33.3%
1 3421
 
7.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
False
9065 
True
5666 
ValueCountFrequency (%)
False 9065
61.5%
True 5666
38.5%
2023-01-21T23:42:17.348176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Interactions

2023-01-21T23:42:08.019502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:43.056673image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:44.916282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:46.703115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:48.615991image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:50.288797image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:52.287716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:54.438055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:56.589482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:58.521933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:00.769368image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:02.613808image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:04.362030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:06.050793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:08.143313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:43.180993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:45.040282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:46.829885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:48.731742image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:50.411839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:52.422758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:54.557279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:56.721205image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:58.645216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:00.897248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:02.737857image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:04.479490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:06.174884image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:08.269424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:43.313232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:45.164776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:46.954059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:48.851753image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:50.535126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:52.564331image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:54.724603image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:56.845760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:58.778161image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:01.056768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:02.855444image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:04.603989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:06.292547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:08.391221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:43.432864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:45.280172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:47.073588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:48.966980image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:50.662642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:52.688947image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:54.928362image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:56.966278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:58.898365image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:01.178469image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:02.973544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:04.716748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:06.412966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:08.508754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:43.559237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:45.396952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:47.191779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:49.081867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:50.785868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:52.806321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:55.138088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:57.084389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:59.079973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:01.293007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:03.087175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:04.826094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:06.774847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:08.634163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:43.707414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:45.526912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:47.379398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:49.198337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:50.919474image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:52.929707image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:55.271979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:57.225517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:59.215463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:01.428145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:03.216300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:04.950816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:06.902229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:08.772753image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:43.847532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:45.655763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:47.499819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:49.318079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:51.051725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:53.055833image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:55.495339image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:57.359530image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:59.344775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:01.579792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:03.338169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:05.073578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:07.024643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:08.929582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:43.969625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:45.783076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:47.630962image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:49.449683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:51.249456image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:53.350341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:55.681489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:57.509868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:59.767474image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:01.751408image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:03.473713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:05.200737image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:07.160481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:09.059400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:44.088303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:45.914281image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:47.754793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:49.572320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:51.430204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:53.485441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:55.827901image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:57.646817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:59.898256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:01.877847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:03.602793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:05.321642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:07.291363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:09.184616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:44.200983image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:46.035061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:47.874756image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:49.688745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:51.569095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:53.619008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:55.951569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:57.781947image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:00.021223image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:01.997273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:03.720102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:05.440553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:07.414220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:09.313122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:44.317179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:46.172003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:47.984528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:49.803668image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:51.707407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:53.790967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:56.075504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:57.912037image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:00.152937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:02.114909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:03.848920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:05.556395image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:07.540939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:09.438588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:44.442398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:46.306427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:48.109355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:49.928405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:51.860432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:54.005198image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:56.203023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:58.078412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:00.314698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:02.235264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:03.967193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:05.675325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:07.660861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:09.566400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:44.554167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:46.430700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:48.217356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:50.043908image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:52.002180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:54.171739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:56.321339image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:58.229470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:00.469982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:02.352881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:04.079661image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:05.788764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:07.774363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:09.707575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:44.798722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:46.567740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:48.346691image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:50.169125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:52.142504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:54.297113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:56.457139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:41:58.387247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:00.600232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:02.483434image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:04.215041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:05.922372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-21T23:42:07.900735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2023-01-21T23:42:17.472631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2023-01-21T23:42:17.725422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-01-21T23:42:17.944062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-01-21T23:42:18.164013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-01-21T23:42:18.372069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-01-21T23:42:18.543454image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-01-21T23:42:09.950537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-21T23:42:10.262067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-21T23:42:10.762199image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

HomePageHomePage_DurationLandingPageLandingPage_DurationProductDescriptionPageProductDescriptionPage_DurationGoogleMetric:Bounce RatesGoogleMetric:Exit RatesGoogleMetric:Page ValuesSeasonalPurchaseMonth_SeasonalPurchaseOSSearchEngineZoneType of TrafficCustomerTypeGenderCookies SettingEducationMarital StatusWeekendPurchaseMade_Purchase
00.00.00.00.01.00.0000000.200.2000000.00.0Feb4.01.09.03.0Returning_VisitorNot SpecifiedDenyNot SpecifiedOther0.0False
10.00.00.00.02.02.6666670.050.1400000.00.0Feb3.02.02.04.0Returning_VisitorFemaleDenyOthersMarried0.0False
20.00.00.00.010.0627.5000000.020.0500000.00.0Feb3.03.01.04.0Returning_VisitorFemaleALLOthersMarried1.0False
30.00.00.00.01.00.0000000.200.2000000.00.4Feb2.04.03.03.0Returning_VisitorMaleALLDiplomaSingle0.0False
41.00.00.00.00.00.0000000.200.2000000.00.0Feb1.02.01.05.0Returning_VisitorMaleDenyDiplomaOther1.0False
50.00.00.00.03.0738.0000000.000.0222220.00.4Feb2.04.01.02.0Returning_VisitorFemaleRequiredNot SpecifiedOther0.0False
60.00.00.00.03.0395.0000000.000.0666670.00.0Feb1.01.03.03.0Returning_VisitorFemaleALLDiplomaMarried0.0False
70.00.00.00.07.0280.5000000.000.0285710.00.0Feb1.01.01.03.0Returning_VisitorFemaleDenyDiplomaOther0.0False
80.00.00.00.06.098.0000000.000.0666670.00.0Feb2.05.01.03.0Returning_VisitorNot SpecifiedDenyGraduateSingle0.0False
90.00.00.00.02.068.0000000.000.1000000.00.0Feb3.02.03.03.0Returning_VisitorMaleRequiredDiplomaSingle0.0False
HomePageHomePage_DurationLandingPageLandingPage_DurationProductDescriptionPageProductDescriptionPage_DurationGoogleMetric:Bounce RatesGoogleMetric:Exit RatesGoogleMetric:Page ValuesSeasonalPurchaseMonth_SeasonalPurchaseOSSearchEngineZoneType of TrafficCustomerTypeGenderCookies SettingEducationMarital StatusWeekendPurchaseMade_Purchase
147210.00.0000000.00.02.032.0000000.0000000.1000000.00.0Feb2.02.01.03.0Returning_VisitorFemaleDenyDiplomaOther0.0True
147221.03.0000000.00.011.0271.7166670.0000000.0166670.00.0Mar4.01.03.02.0Returning_VisitorMaleDenyDiplomaMarried1.0True
147230.00.0000000.00.05.047.0000000.000000NaN0.00.0Nov2.02.03.03.0Returning_VisitorNot SpecifiedALLNot SpecifiedOther0.0True
147241.0194.0000000.00.021.0304.2750000.0000000.0011700.00.0May1.02.04.02.0Returning_VisitorFemaleRequiredOthersOther0.0True
147250.00.0000000.00.04.069.0000000.0000000.0500000.00.0Mar1.01.01.02.0Returning_VisitorNot SpecifiedALLOthersOther0.0True
147261.04.0000000.00.039.0983.1388890.0153850.0175990.00.0Nov3.02.06.03.0Returning_VisitorFemaleDenyNaNMarried0.0True
147278.0117.0238092.057.011.0252.8928570.0000000.0110780.00.0May2.02.02.04.0Returning_VisitorNot SpecifiedALLDiplomaMarried0.0True
147282.075.6000002.0652.810.01143.6666670.0000000.0233330.00.0Aug2.02.04.02.0Returning_VisitorNot SpecifiedRequiredOthersSingle0.0True
147290.00.0000000.00.06.01057.0000000.0000000.0333330.00.0Mar2.04.04.01.0Returning_VisitorNot SpecifiedRequiredNot SpecifiedMarried0.0True
147300.00.0000000.00.021.01372.7000000.0190480.0357140.00.0Mar1.01.03.01.0Returning_VisitorFemaleALLDiplomaOther0.0True

Duplicate rows

Most frequently occurring

HomePageHomePage_DurationLandingPageLandingPage_DurationProductDescriptionPageProductDescriptionPage_DurationGoogleMetric:Bounce RatesGoogleMetric:Exit RatesGoogleMetric:Page ValuesSeasonalPurchaseMonth_SeasonalPurchaseOSSearchEngineZoneType of TrafficCustomerTypeGenderCookies SettingEducationMarital StatusWeekendPurchaseMade_Purchase# duplicates
00.00.00.00.01.00.00.20.20.00.0May1.01.01.03.0Returning_VisitorMaleDenyOthersOther0.0False2